ABSTRACT The United States Preventive Services Task Force (USPSTF) recently graded the use of prostate-specific antigen (PSA) to screen for prostate cancer (PCa) a ?C?: ?For men aged 55 to 69 years, the decision to undergo periodic PSA-based screening for prostate cancer should be an individual one and should include discussion of the potential benefits and harms of screening with their clinician.? The USPSTF decided that PSA screening as it currently exists is inadequate for widespread implementation. It is thus important to ask the question how PSA screening can be improved to reduce overdiagnosis, overtreatment, and PCa mortality. Evidence suggests that the incorporation of genetic factors into PSA screening decisions could do just that. PSA levels have been shown to be highly heritable, but the associated genetic variants that have been identified thus far explain only some of the variation. Moreover, the variation explained is even smaller when dealing with non-European populations. If we can determine the genetic factors that predispose individuals to high PSA levels independently of PCa, we could then account for them as part of a new PSA screening paradigm. We propose to do just this with a large-scale project combining data from 17 studies of men both with and without PCa, all of whom have data on both PSA levels and genome-wide variants. In sum, the studies consist of 653,076 men, including 106,326 men of African ancestry, 35,683 of Latino ancestry, and 10,001 of Asian ancestry. In Aim 1, we will undertake a multi-ancestry genome-wide association study (GWAS) of PSA levels that is 20-times larger than any previous such analysis, as well as the first ever transcriptome- wide association study (TWAS) of PSA levels. We will then leverage the multi-ancestry nature of our sample to discover additional genetic variants associated with PSA via fine mapping. In Aim 2 we will evaluate the independence of the SNPs associated with PSA discovered in the GWAS and the genes associated with PSA discovered in the TWAS using conditional analyses. We will then differentiate between genetic factors associated with PSA and those associated with PCa using conditional and mediation analyses. Based on these findings, we will create and test polygenic risk scores for PSA levels that combine associated genetic factors together into single, powerful measures. Finally, in Aim 3, we will use measured PSA levels and genetic factors?accounting for constitutive, non-PCa variability in PSA?to develop models that more accurately predict PCa outcomes. These models will allow us to assess the benefit of additionally incorporating genetic information with respect to deciding whether a man should undergo prostate biopsy. Our aims in aggregate are promising toward reducing screening harms while improving screening benefits. In translation, clinicians and patients could make more informed decisions, thereby reducing unnecessary procedures and diagnoses, and preventing poor outcomes.